ZZS similarity tool: The online tool for similarity screening to identify chemicals of potential concern

Screening and prioritization of chemicals is essential to ensure that available evaluation capacity is invested in those substances that are of highest concern. We, therefore, recently developed structural similarity models that evaluate the structural similarity of substances with unknown properties to known Substances of Very High Concern (SVHC), which could be an indication of comparable effects. In the current study the performance of these models is improved by (1) separating known SVHCs in more specific subgroups, (2) (re‐)optimizing similarity models for the various SVHC‐subgroups, and (3) improving interpretability of the predicted outcomes by providing a confidence score. The improvements are directly incorporated in a freely accessible web‐based tool, named the ZZS similarity tool: https://rvszoeksysteem.rivm.nl/ZzsSimilarityTool. Accordingly, this tool can be used by risk assessors, academia and industrial partners to screen and prioritize chemicals for further action and evaluation within varying frameworks, and could support the identification of tomorrow's substances of concern.

EViS: An Enhanced Virtual Screening Approach Based on Pocket-Ligand Similarity

Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein-ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket-ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein-ligand structure database.

Computational study for suppression of CD25/IL-2 interaction

Cancer recurrence presents a huge challenge in cancer patient management. Immune escape is a key mechanism of cancer progression and metastatic dissemination. CD25 is expressed in regulatory T (Treg) cells including tumor-infiltrating Treg cells (TI-Tregs). These cells specially activate and reinforce immune escape mechanism of cancers. The suppression of CD25/IL-2 interaction would be useful against Treg cells activation and ultimately immune escape of cancer. Here, software, web servers and databases were used, at which in silico designed small interfering RNAs (siRNAs), de novo designed peptides and virtual screened small molecules against CD25 were introduced for the prospect of eliminating cancer immune escape and obtaining successful treatment. We obtained siRNAs with low off-target effects. Further, small molecules based on the binding homology search in ligand and receptor similarity were introduced. Finally, the critical amino acids on CD25 were targeted by a de novo designed peptide with disulfide bond. Hence we introduced computational-based antagonists to lay a foundation for further in vitro and in vivo studies.

DStruBTarget: Integrating Binding Affinity with Structure Similarity for Ligand-Binding Protein Prediction

Motivation: Identification of ligand-binding proteins is an important issue for drug development. Most of the current computational approach is developed only utilizing ligand structure similarity. However, the ligand structure similarity has failed to reflect the binding quality between the ligand and the target protein, which limited the performance of current methods. Results: The present study integrated two-dimensional (2D) and three-dimensional (3D) ligand structure similarity between query ligand and template with known ligand-protein binding affinity (BA) to identify proteins binding with the query ligand. This method is named as DStruBTarget. The performance of DStruBTarget was evaluated by 10-fold cross-validation in a dataset containing 9197 ligands and 1111 ligand-binding proteins (DBD dataset). This dataset was constructed by excluding the ligands with similar structures and the proteins with high sequence identity. The DStruBTarget achieved a hit rate of 77% in top 1 prediction, which is 4.80 and 3.00% better than the methods only using 2D structure similarity, and the method integrating 2D and 3D structure similarity (2D + 3D), respectively. An independent test of DStruBTarget was performed in a publicly available dataset constructed by SwissTargetPrediction. In this dataset, the top 1 hit rate of DStruBTarget reached 44.02%, which was better than the SwissTargetPrediction, and also outstands other methods, such as 2D, 3D, 2D + 3D, 2D integrating binding affinity (2D + BA), and 3D integrating binding affinity (3D + BA). DStruBTarget was compared to another newly published method HitPickV2 and achieved 52.17% hit rate of the top 1 prediction, which was significantly better than the result of HitpickV2 (30.43%). Finally, DStruBTarget was integrated with protein BLAST to predict the ligand-binding proteins not limited in a certain database. DStruBTarget with BLAST was tested in the DBD dataset. Its top 1 hit rate was 51.15%, which is lower than DStruBTarget without BLAST. Further comparison was on the ligands that bind to multiple numbers of proteins, which illustrated that DStruBTarget with BLAST performed better than without BLAST when the number of binding proteins of the query ligands is larger than six. Meanwhile, the prediction power of the DStruBTarget with BLAST in top 1 prediction was found to be positively correlated with the number of proteins binding with the query ligands, while the top 1 prediction power of DStruBTarget without BLAST was negatively correlated with the number of binding proteins for query ligands. Thus, DStruBTarget with BLAST is a potentially useful approach for predicting novel proteins for ligands that bind to multiple proteins.

DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state

Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2.

Computational Prediction of Carbohydrate-Binding Proteins and Binding Sites

Protein-carbohydrate interaction is essential for biological systems, and carbohydrate-binding proteins (CBPs) are important targets when designing antiviral and anticancer drugs. Due to the high cost and difficulty associated with experimental approaches, many computational methods have been developed as complementary approaches to predict CBPs or carbohydrate-binding sites. However, most of these computational methods are not publicly available. Here, we provide a comprehensive review of related studies and demonstrate our two recently developed bioinformatics methods. The method SPOT-CBP is a template-based method for detecting CBPs based on structure through structural homology search combined with a knowledge-based scoring function. This method can yield model complex structure in addition to accurate prediction of CBPs. Furthermore, it has been observed that similarly accurate predictions can be made using structures from homology modeling, which has significantly expanded its applicability. The other method, SPRINT-CBH, is a de novo approach that predicts binding residues directly from protein sequences by using sequence information and predicted structural properties. This approach does not need structurally similar templates and thus is not limited by the current database of known protein-carbohydrate complex structures. These two complementary methods are available at https://sparks-lab.org. © 2018 by John Wiley & Sons, Inc.

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.

SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library

Motivation

The high cost of drug discovery motivates the development of accurate virtual screening tools. Binding-homology, which takes advantage of known protein-ligand binding pairs, has emerged as a powerful discrimination technique. In order to exploit all available binding data, modelled structures of ligand-binding sequences may be used to create an expanded structural binding template library.

Results

SPOT-Ligand 2 has demonstrated significantly improved screening performance over its previous version by expanding the template library 15 times over the previous one. It also performed better than or similar to other binding-homology approaches on the DUD and DUD-E benchmarks.

Availability and Implementation

The server is available online at http://sparks-lab.org .

Contacts

yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au.

Supplementary information

Supplementary data are available at Bioinformatics online.

Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines

Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew's correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at http://sparks-lab.org/server/SPRINT-CBH .